Abstract: Systems and techniques for providing concurrent image and corresponding multi-channel auxiliary data generation for a generative model are presented. In one example, a system generates synthetic multi-channel data associated with a synthetic version of imaging data. The system also predicts multi-channel imaging data and the synthetic multi-channel data with a first predicted class set or a second predicted class set. Furthermore, the system employs the first predicted class set or the second predicted class set for the synthetic multi-channel data to train a generative adversarial network model.

Abstract: Techniques are described for performing active surveillance and learning for machine learning (ML) model authoring and deployment workflows. In an embodiment, a method comprises applying, by a system comprising a processor, a primary ML model trained on a training dataset to data samples excluded from the training dataset to generate inferences based on the data samples. The method further comprises employing, by the system, one or more active surveillance techniques to regulate performance of the primary ML model in association with the applying, wherein the one or more active surveillance techniques comprise at least one of, performing a model scope evaluation of the primary ML model relative to the data samples or using a domain adapted version of the primary ML model to generate the inferences.

Abstract: Techniques are provided for enhancing the efficiency and accuracy of annotating data samples for supervised machine learning algorithms using an advanced annotation pipeline. According to an embodiment, a method can comprise collecting, by a system comprising a processor, unannotated data samples for input to a machine learning model and storing the unannotated data samples in an annotation queue. The method further comprises determining, by the system, annotation priority levels for respective unannotated data samples of the unannotated data samples, selecting, by the system from amongst different annotation techniques, one or more of the different annotation techniques for annotating the respective unannotated data samples based the annotation priority levels associated with the respective unannotated data samples.

Abstract: Techniques are provided for enhancing the efficiency and accuracy of annotating data samples for supervised machine learning algorithms using an advanced annotation pipeline. According to an embodiment, a method can comprise collecting, by a system comprising a processor, unannotated data samples for input to a machine learning model and storing the unannotated data samples in an annotation queue. The method further comprises determining, by the system, annotation priority levels for respective unannotated data samples of the unannotated data samples, selecting, by the system from amongst different annotation techniques, one or more of the different annotation techniques for annotating the respective unannotated data samples based the annotation priority levels associated with the respective unannotated data samples.

Abstract: Techniques are provided for determining confident data samples for machine learning (ML) models on unseen data. In one embodiment, a method is provided that comprises extracting, by a system comprising a processor, a feature vector for a data sample based on projection of the data sample onto a standard feature space. The method further comprises processing, by the system, the feature vector using an outlier detection model to determine whether the data sample is within a scope of a training dataset used to train a machine learning model, wherein the outlier detection model was trained using features extracted from the training dataset based on projection of data samples included in the training dataset onto the standard feature space.

Abstract: Systems, apparatus, instructions, and methods for medical machine time-series event data generation are disclosed. An example synthetic time series data generation apparatus is to generate a synthetic data set including multi-channel time-series data and associated annotation using a first artificial intelligence network model. The example apparatus is to analyze the synthetic data set with respect to a real data set using a second artificial intelligence network model. When the second artificial intelligence network model classifies the synthetic data set as a first classification, the example apparatus is to adjust the first artificial intelligence network model using feedback from the second artificial intelligence network model. When the second artificial intelligence network model classifies the synthetic data set as a second classification, the example apparatus is to output the synthetic data set.

Abstract: Systems, apparatus, instructions, and methods for medical machine time-series event data processing are disclosed. An example apparatus includes a data processor to process one-dimensional data captured over time with respect to patient(s). The example apparatus includes a visualization processor to transform the processed data into graphical representations and to cluster the graphical representations including the first graphical representation into at least first and second blocks arranged with respect to an indicator of a criterion to provide a visual comparison of the first block and the second block with respect to the criterion. The example apparatus includes an interaction processor to facilitate interaction, via the graphical user interface, with the first and second blocks of graphical representations to extract a data set for processing from at least a subset of the first and second blocks.

Abstract: An artificial intelligence platform and associated methods of training and use are disclosed. An example apparatus includes a data pipeline to: preprocess data using one or more preprocessing operations applied to features associated with the data; and enable debugging to visualize the preprocessed data. The example apparatus includes a network to: instantiate one or more differentiable operations in a training configuration to train an artificial intelligence model; capture feedback including optimization and loss information to adjust the training configuration; and store one or more metrics to evaluate performance of the artificial intelligence model.

Abstract: Techniques are provided for deep neural network (DNN) identification of realistic synthetic images generated using a generative adversarial network (GAN). According to an embodiment, a system is described that can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise, a first extraction component that extracts a subset of synthetic images classified as non-real like as opposed to real-like, wherein the subset of synthetic images were generated using a GAN model.

Abstract: Techniques are provided for evaluating and defining the scope of data-driven deep learning models. In one embodiment, a machine-readable storage medium is provided comprising executable instructions that, when executed by a processor, facilitate performance of operations comprising employing a machine learning model to extract first training data features included in a training data set and first target data features included in a target data set. The operations further comprise determining whether the target data set is within a defined data scope of the training data set based on analysis of correspondences between the first training data features and the first target data feature, and determining whether application of the target data set to a target neural network model developed using the training data set will generate results with an acceptable level of accuracy based on whether the target data set is within the defined data scope.

Abstract: Systems and techniques for providing concurrent image and corresponding multi-channel auxiliary data generation for a generative model are presented. In one example, a system generates synthetic multi-channel data associated with a synthetic version of imaging data. The system also predicts multi-channel imaging data and the synthetic multi-channel data with a first predicted class set or a second predicted class set. Furthermore, the system employs the first predicted class set or the second predicted class set for the synthetic multi-channel data to train a generative adversarial network model.

Abstract: Systems and computer-implemented methods for facilitating automated compression of artificial neural networks using an iterative hybrid reinforcement learning approach are provided. In various embodiments, a compression architecture can receive as input an original neural network to be compressed. The architecture can perform one or more compression actions to compress the original neural network into a compressed neural network. The architecture can then generate a reward signal quantifying how well the original neural network was compressed. In (?)-proportion of compression iterations/episodes, where ??[0,1], the reward signal can be computed in model-free fashion based on a compression ratio and accuracy ratio of the compressed neural network. In (1??)-proportion of compression iterations/episodes, the reward signal can be predicted in model-based fashion using a compression model learned/trained on the reward signals computed in model-free fashion.

Abstract: Systems, methods and apparatus are provided through which in some implementations changes in an aneurysm in a patient over time are identified by determining temporal differences between segmented aneurysms in a plurality of longitudinal exams and visually presenting the temporal differences.

Abstract: The present invention is a system and method of remote patient monitoring to allow a patient to initiate and activate sensing systems. In the system and method, standard parameters can be sensed, and the information can then be processed and sent to the physician or clinician. The clinician then has the ability to remotely configure or reconfigure the parameters of the sensing system so as to probe for more targeted information based on the initial sensed data.

Abstract: A method includes automatically determining at least one gating signal based on a physiological signal from a subject being imaged by an imaging system, automatically determining, based upon prior analysis and knowledge of the imaging system's capabilities, a timing of each of a plurality of exposures within a single or multiple cycles of the physiologic signal, and performing the multiple acquisitions.

Abstract: Certain embodiments of the present invention provide a system and method for synchronized viewing of a plurality of images of an object. Corresponding landmarks of an object are synchronized between a first image set and a second image set. In an embodiment, the landmarks are folds of a human colon and the first image set and second images sets are computerized tomography scans, at least one image set being a prone scan of a portion of anatomy and at least one image set being a supine scan of a portion of the anatomy. An indicator for at least a first location in a first image set is displayed. The location of a second location in a second image set of an object is determined, wherein the second location corresponds to the first location of the object. The second location in the second image set is displayed.

Abstract: A system, method, and apparatus includes a computer readable storage medium with a computer program stored thereon having instructions that cause a computer to access a first anatomical image data set of an imaging subject acquired via a morphological imaging modality, access a functional image data set of the imaging subject acquired via a functional imaging modality, register the first anatomical image data set to the functional image data set, segment the functional image data set based on the functional image data set, define a binary mask based on the segmented functional image data set, and apply the binary mask to the first anatomical image data set to construct a second anatomical image data set and an image based thereon. The second anatomical image data set is substantially free of image data of the first anatomical image data set correlating to an area outside the region of physiological activity.

Abstract: A data processing technique is provided. In one embodiment, a computer-implemented method includes accessing reference deviation maps for a plurality of disease types. The reference deviation maps may include subsets of maps associated with severity levels of respective disease types, and a disease severity score may be associated with each severity level. The method may include selecting patient severity levels for multiple disease types based on the subsets of reference deviation maps. Also, the method may include automatically calculating a combined patient disease severity score based at least in part on the disease severity scores associated with the selected patient severity levels, and may include outputting a report based at least in part on the combined patient disease severity score. Additional methods, systems, and manufactures are also disclosed.

Abstract: A technique is disclosed for servicing complex systems. Upon detection of a serviceable condition, a system snapshot is made and stored. The snapshot may include a range of data then available on the system, particularly hardware configuration data, such as components then installed, peripherals installed, their states, and so forth. The snapshot may include data available prior to the detection of an indicator, at the time of detection and following detection, facilitating evaluation of the condition and potential responses to it.

Abstract: Certain embodiments of the present invention provide a method and system for improved clinical workflow using wireless communication. A system for remote image display includes a data source with image data, wherein the data source is capable of transmitting the image data. The system also includes an identifiable display device capable of displaying image data transferred from the data source and a portable device capable of identifying the display device and requesting image data transfer from the data source to the display device without the transfer of the image data between the portable device and the data source. The system may also include an access point for relaying communication between the portable device and the data source. Communication between the portable device, the data source, and/or the display may include wireless communication, for example.